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arXiv · 1905.06455

On Norm-Agnostic Robustness of Adversarial Training

Bai Li·Changyou Chen·Wenlin Wang·Lawrence Carin
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Abstract

Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where adversarial examples are injected into training data to increase robustness. In this paper, we propose a new attack to unveil an undesired property of the state-of-the-art adversarial training, that is it fails to obtain robustness against perturbations in $\ell_2$ and $\ell_\infty$ norms simultaneously. We discuss a possible solution to this issue and its limitations as well.

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Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin. 2019-05-15. On Norm-Agnostic Robustness of Adversarial Training. https://arxiv.org/abs/1905.06455

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